Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Self-supervised vision transformers learn visual concepts in histopathology
RJ Chen, RG Krishnan - arXiv preprint arXiv:2203.00585, 2022 - arxiv.org
Tissue phenotyping is a fundamental task in learning objective characterizations of
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
A general-purpose self-supervised model for computational pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides
Cancer diagnosis and management depend upon the extraction of complex information from
microscopy images by pathologists, which requires time-consuming expert interpretation …
microscopy images by pathologists, which requires time-consuming expert interpretation …
Towards a generalizable pathology foundation model via unified knowledge distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of
computational pathology (CPath). The generalization ability of foundation models is crucial …
computational pathology (CPath). The generalization ability of foundation models is crucial …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models
F Carrillo-Perez, M Pizurica, MG Ozawa, H Vogel… - Cell Reports …, 2023 - cell.com
In this work, we propose an approach to generate whole-slide image (WSI) tiles by using
deep generative models infused with matched gene expression profiles. First, we train a …
deep generative models infused with matched gene expression profiles. First, we train a …
GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation
V Purma, S Srinath, S Srirangarajan… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Histopathological image segmentation is a laborious and time-intensive task, often requiring
analysis from experienced pathologists for accurate examinations. To reduce this burden …
analysis from experienced pathologists for accurate examinations. To reduce this burden …
Synthetic Privileged Information Enhances Medical Image Representation Learning
L Farndale, C Walsh, R Insall, K Yuan - arXiv preprint arXiv:2403.05220, 2024 - arxiv.org
Multimodal self-supervised representation learning has consistently proven to be a highly
effective method in medical image analysis, offering strong task performance and producing …
effective method in medical image analysis, offering strong task performance and producing …
Stain-invariant self supervised learning for histopathology image analysis
We present a self-supervised algorithm for several classification tasks within hematoxylin
and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations …
and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations …